importing required files¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import warnings
warnings.filterwarnings('ignore')
In [2]:
print (os.getcwd())
C:\Users\Dell
In [3]:
os.chdir ('C:\\Users\\Dell\\OneDrive\\Desktop\\CAR Price Prediction')
In [4]:
print (os.getcwd())
C:\Users\Dell\OneDrive\Desktop\CAR Price Prediction

reading data¶

In [5]:
data=pd.read_csv("C:\\Users\\Dell\\OneDrive\\Desktop\\excel books\\audi.csv")
display(data)
model year price transmission dist_travelled fuelType tax mpg engineSize
0 A1 2017 12500 Manual 15735 Petrol 150 55.4 1.4
1 A6 2016 16500 Automatic 36203 Diesel 20 64.2 2.0
2 A1 2016 11000 Manual 29946 Petrol 30 55.4 1.4
3 A4 2017 16800 Automatic 25952 Diesel 145 67.3 2.0
4 A3 2019 17300 Manual 1998 Petrol 145 49.6 1.0
... ... ... ... ... ... ... ... ... ...
10663 A3 2020 16999 Manual 4018 Petrol 145 49.6 1.0
10664 A3 2020 16999 Manual 1978 Petrol 150 49.6 1.0
10665 A3 2020 17199 Manual 609 Petrol 150 49.6 1.0
10666 Q3 2017 19499 Automatic 8646 Petrol 150 47.9 1.4
10667 Q3 2016 15999 Manual 11855 Petrol 150 47.9 1.4

10668 rows × 9 columns

In [6]:
import pandas_profiling as pp
In [7]:
display(pp.ProfileReport(data))
Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

manual data Exploration¶

In [8]:
print(len(data))
10668
In [9]:
print(data.shape)
(10668, 9)
In [10]:
display (data.dtypes )
model              object
year                int64
price               int64
transmission       object
dist_travelled      int64
fuelType           object
tax                 int64
mpg               float64
engineSize        float64
dtype: object
In [11]:
display (data.isna().sum() )
model             0
year              0
price             0
transmission      0
dist_travelled    0
fuelType          0
tax               0
mpg               0
engineSize        0
dtype: int64
In [12]:
print (data.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10668 entries, 0 to 10667
Data columns (total 9 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   model           10668 non-null  object 
 1   year            10668 non-null  int64  
 2   price           10668 non-null  int64  
 3   transmission    10668 non-null  object 
 4   dist_travelled  10668 non-null  int64  
 5   fuelType        10668 non-null  object 
 6   tax             10668 non-null  int64  
 7   mpg             10668 non-null  float64
 8   engineSize      10668 non-null  float64
dtypes: float64(2), int64(4), object(3)
memory usage: 750.2+ KB
None
In [13]:
display (data.describe ())
year price dist_travelled tax mpg engineSize
count 10668.000000 10668.000000 10668.000000 10668.000000 10668.000000 10668.000000
mean 2017.100675 22896.685039 24827.244001 126.011436 50.770022 1.930709
std 2.167494 11714.841888 23505.257205 67.170294 12.949782 0.602957
min 1997.000000 1490.000000 1.000000 0.000000 18.900000 0.000000
25% 2016.000000 15130.750000 5968.750000 125.000000 40.900000 1.500000
50% 2017.000000 20200.000000 19000.000000 145.000000 49.600000 2.000000
75% 2019.000000 27990.000000 36464.500000 145.000000 58.900000 2.000000
max 2020.000000 145000.000000 323000.000000 580.000000 188.300000 6.300000
In [14]:
data
Out[14]:
model year price transmission dist_travelled fuelType tax mpg engineSize
0 A1 2017 12500 Manual 15735 Petrol 150 55.4 1.4
1 A6 2016 16500 Automatic 36203 Diesel 20 64.2 2.0
2 A1 2016 11000 Manual 29946 Petrol 30 55.4 1.4
3 A4 2017 16800 Automatic 25952 Diesel 145 67.3 2.0
4 A3 2019 17300 Manual 1998 Petrol 145 49.6 1.0
... ... ... ... ... ... ... ... ... ...
10663 A3 2020 16999 Manual 4018 Petrol 145 49.6 1.0
10664 A3 2020 16999 Manual 1978 Petrol 150 49.6 1.0
10665 A3 2020 17199 Manual 609 Petrol 150 49.6 1.0
10666 Q3 2017 19499 Automatic 8646 Petrol 150 47.9 1.4
10667 Q3 2016 15999 Manual 11855 Petrol 150 47.9 1.4

10668 rows × 9 columns

In [15]:
data.drop_duplicates(subset=['model','year','price','transmission','dist_travelled','fuelType','tax','mpg','engineSize'],inplace=True,keep='first')
In [16]:
data.shape
Out[16]:
(10565, 9)
In [17]:
X = data.iloc[:,[0,1,3,4,5,6,7,8]].values
display (X.shape)
display (X)
(10565, 8)
array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
       [' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
       [' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)
In [18]:
data.head()
Out[18]:
model year price transmission dist_travelled fuelType tax mpg engineSize
0 A1 2017 12500 Manual 15735 Petrol 150 55.4 1.4
1 A6 2016 16500 Automatic 36203 Diesel 20 64.2 2.0
2 A1 2016 11000 Manual 29946 Petrol 30 55.4 1.4
3 A4 2017 16800 Automatic 25952 Diesel 145 67.3 2.0
4 A3 2019 17300 Manual 1998 Petrol 145 49.6 1.0
In [19]:
Y = data.iloc[:,[2]].values
display (Y.shape)
display (Y)
(10565, 1)
array([[12500],
       [16500],
       [11000],
       ...,
       [17199],
       [19499],
       [15999]], dtype=int64)
In [20]:
display(pd.DataFrame(X).head(5))
0 1 2 3 4 5 6 7
0 A1 2017 Manual 15735 Petrol 150 55.4 1.4
1 A6 2016 Automatic 36203 Diesel 20 64.2 2.0
2 A1 2016 Manual 29946 Petrol 30 55.4 1.4
3 A4 2017 Automatic 25952 Diesel 145 67.3 2.0
4 A3 2019 Manual 1998 Petrol 145 49.6 1.0

Label Encoding¶

In [21]:
from sklearn.preprocessing import LabelEncoder
In [22]:
le1 = LabelEncoder()
X[:,0] = le1.fit_transform(X[:,0])
le2 = LabelEncoder()
X[:,-4] = le2.fit_transform(X[:,-4])
display (X)
array([[0, 2017, 'Manual', ..., 150, 55.4, 1.4],
       [5, 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [0, 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [2, 2020, 'Manual', ..., 150, 49.6, 1.0],
       [9, 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [9, 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)

one hot encoding¶

In [23]:
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers = [('encoder',OneHotEncoder(),[2])],remainder='passthrough')
X = ct.fit_transform(X)
display (X.shape)
display (pd.DataFrame(X))
(10565, 10)
0 1 2 3 4 5 6 7 8 9
0 0.0 1.0 0.0 0 2017 15735 2 150 55.4 1.4
1 1.0 0.0 0.0 5 2016 36203 0 20 64.2 2.0
2 0.0 1.0 0.0 0 2016 29946 2 30 55.4 1.4
3 1.0 0.0 0.0 3 2017 25952 0 145 67.3 2.0
4 0.0 1.0 0.0 2 2019 1998 2 145 49.6 1.0
... ... ... ... ... ... ... ... ... ... ...
10560 0.0 1.0 0.0 2 2020 4018 2 145 49.6 1.0
10561 0.0 1.0 0.0 2 2020 1978 2 150 49.6 1.0
10562 0.0 1.0 0.0 2 2020 609 2 150 49.6 1.0
10563 1.0 0.0 0.0 9 2017 8646 2 150 47.9 1.4
10564 0.0 1.0 0.0 9 2016 11855 2 150 47.9 1.4

10565 rows × 10 columns

Standardizing the data¶

In [24]:
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
display (pd.DataFrame(X))
0 1 2 3 4 5 6 7 8 9
0 -0.582997 1.203038 -0.714096 -1.119276 -0.039002 -0.393254 1.053589 0.357402 0.351966 -0.884062
1 1.715274 -0.831229 -0.714096 -0.158819 -0.500425 0.479662 -0.951665 -1.571222 1.030836 0.111173
2 -0.582997 1.203038 -0.714096 -1.119276 -0.500425 0.212815 1.053589 -1.422867 0.351966 -0.884062
3 1.715274 -0.831229 -0.714096 -0.543002 -0.039002 0.042479 -0.951665 0.283224 1.269983 0.111173
4 -0.582997 1.203038 -0.714096 -0.735093 0.883845 -0.979108 1.053589 0.283224 -0.095471 -1.547551
... ... ... ... ... ... ... ... ... ... ...
10560 -0.582997 1.203038 -0.714096 -0.735093 1.345269 -0.892959 1.053589 0.283224 -0.095471 -1.547551
10561 -0.582997 1.203038 -0.714096 -0.735093 1.345269 -0.979961 1.053589 0.357402 -0.095471 -1.547551
10562 -0.582997 1.203038 -0.714096 -0.735093 1.345269 -1.038346 1.053589 0.357402 -0.095471 -1.547551
10563 1.715274 -0.831229 -0.714096 0.609547 -0.039002 -0.695585 1.053589 0.357402 -0.226616 -0.884062
10564 -0.582997 1.203038 -0.714096 0.609547 -0.500425 -0.558728 1.053589 0.357402 -0.226616 -0.884062

10565 rows × 10 columns

spliting data to train and test the model¶

In [25]:
from sklearn.model_selection import train_test_split

(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)


print (X_train.shape, Y_train.shape)
print(Y_test.shape)
(8452, 10) (8452, 1)
(2113, 1)
In [ ]:
 

using regression¶

In [26]:
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train,Y_train)
Out[26]:
LinearRegression()
In [ ]:
 
In [27]:
y_pred = reg.predict(X_test)
display (y_pred.shape)
(2113, 1)
In [ ]:
 
In [28]:
print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))
[[31863.18288911 34991.        ]
 [19374.13511826 17299.        ]
 [13295.65796165 11444.        ]
 ...
 [18373.48622929 17670.        ]
 [20230.20801119 14290.        ]
 [17652.48622929 18990.        ]]
In [ ]:
 

checking for accuracy¶

In [29]:
from sklearn.metrics import r2_score,mean_absolute_error
print('R2 Score ', r2_score(Y_test, y_pred))
print('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
R2 Score  0.7941818440937328
Mean Absolute Error 3244.810815892843
In [30]:
y_pred = reg.predict(X)
display (y_pred)
y_pred=y_pred[:2113,0]
array([[14861.39031251],
       [20407.69760853],
       [13617.39031251],
       ...,
       [19728.17779394],
       [21238.75145114],
       [16811.25145114]])
In [31]:
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1)
display( result.head())
result.rename(columns={0: 'pred_price'}, inplace=True)
display( result)
model year price transmission dist_travelled fuelType tax mpg engineSize 0
0 A1 2017.0 12500.0 Manual 15735.0 Petrol 150.0 55.4 1.4 14861.390313
1 A6 2016.0 16500.0 Automatic 36203.0 Diesel 20.0 64.2 2.0 20407.697609
2 A1 2016.0 11000.0 Manual 29946.0 Petrol 30.0 55.4 1.4 13617.390313
3 A4 2017.0 16800.0 Automatic 25952.0 Diesel 145.0 67.3 2.0 20163.341671
4 A3 2019.0 17300.0 Manual 1998.0 Petrol 145.0 49.6 1.0 17648.177794
model year price transmission dist_travelled fuelType tax mpg engineSize pred_price
0 A1 2017.0 12500.0 Manual 15735.0 Petrol 150.0 55.4 1.4 14861.390313
1 A6 2016.0 16500.0 Automatic 36203.0 Diesel 20.0 64.2 2.0 20407.697609
2 A1 2016.0 11000.0 Manual 29946.0 Petrol 30.0 55.4 1.4 13617.390313
3 A4 2017.0 16800.0 Automatic 25952.0 Diesel 145.0 67.3 2.0 20163.341671
4 A3 2019.0 17300.0 Manual 1998.0 Petrol 145.0 49.6 1.0 17648.177794
... ... ... ... ... ... ... ... ... ... ...
1162 NaN NaN NaN NaN NaN NaN NaN NaN NaN 20095.841671
1563 NaN NaN NaN NaN NaN NaN NaN NaN NaN 7677.746250
1564 NaN NaN NaN NaN NaN NaN NaN NaN NaN 22037.069150
1874 NaN NaN NaN NaN NaN NaN NaN NaN NaN 21134.659596
1875 NaN NaN NaN NaN NaN NaN NaN NaN NaN 13741.697609

10578 rows × 10 columns

In [32]:
data
Out[32]:
model year price transmission dist_travelled fuelType tax mpg engineSize
0 A1 2017 12500 Manual 15735 Petrol 150 55.4 1.4
1 A6 2016 16500 Automatic 36203 Diesel 20 64.2 2.0
2 A1 2016 11000 Manual 29946 Petrol 30 55.4 1.4
3 A4 2017 16800 Automatic 25952 Diesel 145 67.3 2.0
4 A3 2019 17300 Manual 1998 Petrol 145 49.6 1.0
... ... ... ... ... ... ... ... ... ...
10663 A3 2020 16999 Manual 4018 Petrol 145 49.6 1.0
10664 A3 2020 16999 Manual 1978 Petrol 150 49.6 1.0
10665 A3 2020 17199 Manual 609 Petrol 150 49.6 1.0
10666 Q3 2017 19499 Automatic 8646 Petrol 150 47.9 1.4
10667 Q3 2016 15999 Manual 11855 Petrol 150 47.9 1.4

10565 rows × 9 columns

In [33]:
actual=data.iloc[:2113,2]
actual
distance=data.iloc[:2113,4]
print(distance)
0       15735
1       36203
2       29946
3       25952
4        1998
        ...  
2121    52595
2122    25000
2123    31292
2124    49652
2125    15016
Name: dist_travelled, Length: 2113, dtype: int64
In [34]:
print(distance.shape)
print(y_pred.shape)
(2113,)
(2113,)
In [35]:
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()
In [ ]:
 

using random forest regressor¶

In [ ]:
 
In [ ]:
 
In [36]:
from sklearn.ensemble import RandomForestRegressor
regression = RandomForestRegressor(random_state=0)
regression.fit(X_train,Y_train)
display (regression)
RandomForestRegressor(random_state=0)
In [37]:
from sklearn.model_selection import train_test_split

(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)


print (X_train.shape, Y_train.shape)
print (X_test.shape, Y_test.shape)
(8452, 10) (8452, 1)
(2113, 10) (2113, 1)
In [38]:
y_pred = regression.predict(X_test)
display (y_pred)
array([34565.81, 16820.73, 11530.84, ..., 18497.45, 17153.97, 18620.66])
In [39]:
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1)
display( result.tail(50))
result.rename(columns={0: 'pred_price'}, inplace=True)
display( result)
model year price transmission dist_travelled fuelType tax mpg engineSize 0
10631 TT 2012.0 10490.0 Manual 24693.0 Diesel 165.0 51.4 2.0 NaN
10632 A1 2010.0 9990.0 Automatic 38000.0 Petrol 125.0 53.3 1.4 NaN
10633 A4 2018.0 26891.0 Automatic 22414.0 Petrol 145.0 36.7 3.0 NaN
10634 Q7 2017.0 45595.0 Automatic 28949.0 Diesel 145.0 39.2 4.0 NaN
10635 A3 2016.0 18000.0 Automatic 29494.0 Petrol 125.0 49.6 2.0 NaN
10636 A1 2013.0 9291.0 Manual 29382.0 Petrol 125.0 53.3 1.4 NaN
10637 A5 2017.0 21291.0 Automatic 29666.0 Diesel 30.0 65.7 2.0 NaN
10638 A4 2017.0 18491.0 Automatic 17900.0 Petrol 145.0 50.4 1.4 NaN
10639 A6 2020.0 28000.0 Automatic 2511.0 Diesel 145.0 47.9 2.0 NaN
10640 Q5 2020.0 37000.0 Automatic 1436.0 Petrol 145.0 32.1 2.0 NaN
10641 A5 2020.0 25000.0 Automatic 751.0 Petrol 145.0 40.4 2.0 NaN
10642 Q5 2019.0 33000.0 Automatic 5207.0 Diesel 145.0 38.2 2.0 NaN
10643 A4 2019.0 30000.0 Automatic 9900.0 Diesel 145.0 49.6 2.0 NaN
10644 A5 2019.0 25000.0 Automatic 8571.0 Diesel 145.0 46.3 2.0 NaN
10645 A1 2016.0 10999.0 Manual 22150.0 Diesel 0.0 76.3 1.6 NaN
10646 A1 2016.0 12380.0 Manual 40119.0 Petrol 30.0 55.4 1.4 NaN
10647 A3 2015.0 21000.0 Automatic 12084.0 Petrol 205.0 39.8 2.0 NaN
10648 RS6 2016.0 49990.0 Automatic 24000.0 Petrol 325.0 29.4 4.0 NaN
10649 A3 2009.0 3750.0 Manual 120000.0 Diesel 145.0 53.3 2.0 NaN
10650 A4 2011.0 6995.0 Manual 88000.0 Diesel 30.0 61.4 2.0 NaN
10651 A3 2011.0 9695.0 Manual 32300.0 Petrol 235.0 39.2 2.0 NaN
10652 A1 2014.0 9995.0 Manual 54000.0 Petrol 30.0 55.4 1.2 NaN
10653 A3 2017.0 12995.0 Manual 23820.0 Petrol 145.0 60.1 1.0 NaN
10654 A3 2016.0 16495.0 Semi-Auto 46600.0 Diesel 125.0 57.6 2.0 NaN
10655 S4 2018.0 29995.0 Automatic 29000.0 Petrol 150.0 35.8 3.0 NaN
10656 A3 2016.0 15495.0 Semi-Auto 52500.0 Hybrid 0.0 176.6 1.4 NaN
10657 A4 2016.0 20995.0 Semi-Auto 23700.0 Diesel 30.0 61.4 2.0 NaN
10658 A3 2016.0 14995.0 Manual 39750.0 Petrol 30.0 57.6 1.4 NaN
10659 A6 2018.0 27995.0 Semi-Auto 27500.0 Petrol 150.0 39.8 2.0 NaN
10660 A4 2011.0 9995.0 Automatic 78000.0 Diesel 305.0 39.8 3.0 NaN
10661 A4 2011.0 6995.0 Manual 95000.0 Diesel 145.0 53.3 2.0 NaN
10662 A3 2013.0 12695.0 Manual 31500.0 Petrol 125.0 53.3 1.4 NaN
10663 A3 2020.0 16999.0 Manual 4018.0 Petrol 145.0 49.6 1.0 NaN
10664 A3 2020.0 16999.0 Manual 1978.0 Petrol 150.0 49.6 1.0 NaN
10665 A3 2020.0 17199.0 Manual 609.0 Petrol 150.0 49.6 1.0 NaN
10666 Q3 2017.0 19499.0 Automatic 8646.0 Petrol 150.0 47.9 1.4 NaN
10667 Q3 2016.0 15999.0 Manual 11855.0 Petrol 150.0 47.9 1.4 NaN
273 NaN NaN NaN NaN NaN NaN NaN NaN NaN 21563.460
764 NaN NaN NaN NaN NaN NaN NaN NaN NaN 20608.700
784 NaN NaN NaN NaN NaN NaN NaN NaN NaN 31939.952
967 NaN NaN NaN NaN NaN NaN NaN NaN NaN 119217.800
990 NaN NaN NaN NaN NaN NaN NaN NaN NaN 6965.660
1133 NaN NaN NaN NaN NaN NaN NaN NaN NaN 12045.820
1137 NaN NaN NaN NaN NaN NaN NaN NaN NaN 17817.460
1146 NaN NaN NaN NaN NaN NaN NaN NaN NaN 21198.890
1162 NaN NaN NaN NaN NaN NaN NaN NaN NaN 44255.550
1563 NaN NaN NaN NaN NaN NaN NaN NaN NaN 32483.820
1564 NaN NaN NaN NaN NaN NaN NaN NaN NaN 33188.750
1874 NaN NaN NaN NaN NaN NaN NaN NaN NaN 12127.800
1875 NaN NaN NaN NaN NaN NaN NaN NaN NaN 25382.800
model year price transmission dist_travelled fuelType tax mpg engineSize pred_price
0 A1 2017.0 12500.0 Manual 15735.0 Petrol 150.0 55.4 1.4 34565.810000
1 A6 2016.0 16500.0 Automatic 36203.0 Diesel 20.0 64.2 2.0 16820.730000
2 A1 2016.0 11000.0 Manual 29946.0 Petrol 30.0 55.4 1.4 11530.840000
3 A4 2017.0 16800.0 Automatic 25952.0 Diesel 145.0 67.3 2.0 24245.476667
4 A3 2019.0 17300.0 Manual 1998.0 Petrol 145.0 49.6 1.0 15256.480000
... ... ... ... ... ... ... ... ... ... ...
1162 NaN NaN NaN NaN NaN NaN NaN NaN NaN 44255.550000
1563 NaN NaN NaN NaN NaN NaN NaN NaN NaN 32483.820000
1564 NaN NaN NaN NaN NaN NaN NaN NaN NaN 33188.750000
1874 NaN NaN NaN NaN NaN NaN NaN NaN NaN 12127.800000
1875 NaN NaN NaN NaN NaN NaN NaN NaN NaN 25382.800000

10578 rows × 10 columns

checking for accuracy¶

In [40]:
from sklearn.metrics import r2_score,mean_absolute_error
print  ('R2 Score ', r2_score(Y_test, y_pred))
print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
R2 Score  0.9587306545197221
Mean Absolute Error 1517.4627789258122
In [ ]:
 
In [41]:
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()
In [ ]:
 

using extra tree Regressor¶

In [42]:
from sklearn.ensemble import  ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators=120)
ET_Model.fit(X_train,Y_train)
y_predict=ET_Model.predict(X_test)
import numpy as np

checking for Accuracy¶

In [43]:
from sklearn.metrics import r2_score,mean_absolute_error
print('R2 Score :', r2_score(Y_test, y_predict))
print ('Mean Absolute Error:', mean_absolute_error(Y_test,y_predict))
R2 Score : 0.9625678491519055
Mean Absolute Error: 1517.800980044171
In [44]:
'''y_pred = reg.predict(X)
display (y_pred)
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1).head()
display( result)'''
Out[44]:
'y_pred = reg.predict(X)\ndisplay (y_pred)\nresult = pd.concat([data,pd.DataFrame(y_pred)],axis=1).head()\ndisplay( result)'
In [45]:
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,Y_test,color='red')
plt.scatter(distance,y_predict,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()
In [ ]:

In [ ]:
 

using randomized search cv on Random forest regressor¶

In [46]:
### Randomized search CV
In [47]:
from sklearn.model_selection import RandomizedSearchCV
In [48]:
n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]
In [49]:
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]
In [50]:
rand_grid={'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf}
In [51]:
rf=RandomForestRegressor()
In [52]:
rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)
In [53]:
rCV.fit(X_train,Y_train)
Out[53]:
RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')
In [54]:
rf_pred=rCV.predict(X_test)
display (rf_pred)
array([34363.68578869, 16651.55211445, 11707.03009425, ...,
       18883.21945933, 16724.75648133, 18457.28272006])
In [55]:
rf_pred=rCV.predict(X_test)
display (rf_pred)
array([34363.68578869, 16651.55211445, 11707.03009425, ...,
       18883.21945933, 16724.75648133, 18457.28272006])
In [56]:
from sklearn.metrics import mean_absolute_error,mean_squared_error
print('MAE',mean_absolute_error(Y_test,rf_pred))
print('MSE',mean_squared_error(Y_test,rf_pred))
MAE 1505.2773269897075
MSE 6100595.4818421

checking for accuracy¶

In [57]:
display (r2_score(Y_test,rf_pred))
0.9580830027921906
In [58]:
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,rf_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()

using Cat boost¶

In [59]:
from catboost import CatBoostRegressor
cat=CatBoostRegressor()
print (cat.fit(X_train,Y_train))
Learning rate set to 0.057364
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<catboost.core.CatBoostRegressor object at 0x000001B09DF8F8E0>
In [60]:
cat_pred=cat.predict(X_test)
display (cat_pred)
array([34403.2885362 , 18068.56325027, 11918.19730173, ...,
       18621.48599089, 16917.10863635, 18220.58207119])
In [61]:
display (r2_score(Y_test,cat_pred))
print('MAE',mean_absolute_error(Y_test,cat_pred))
print('MSE', mean_squared_error(Y_test,cat_pred))
0.9621533622246573
MAE 1440.7720299246405
MSE 5508195.786795388
In [62]:
plt.plot(cat_pred,label='pred')
plt.plot(Y_test,label='Actual')
plt.legend('a','b')
plt.show()
In [63]:
%matplotlib inline
plt.figure(figsize=(18,5))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,cat_pred,color='blue')
plt.title('ACTUAL PRICE VS PREDICTED PRICE')
plt.legend(['Actual price ','Predicted Price'])
plt.show()
In [ ]:
 

using knn regressor¶

In [64]:
'''from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error'''
Out[64]:
'from sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.metrics import mean_squared_error'
In [65]:
#X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
In [66]:
#knn_regressor = KNeighborsRegressor(n_neighbors=4)
In [67]:
#knn_regressor.fit(X_train, y_train)
In [68]:
#y_pred = knn_regressor.predict(X_test)
#print(y_pred)
In [69]:
#mse = mean_squared_error(y_test, y_pred)
#print(f'Mean Squared Error: {mse}')
In [70]:
#from sklearn.metrics import r2_score,mean_absolute_error
#print  ('R2 Score ', r2_score(Y_test, y_pred))
#print  ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
In [71]:
'''%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()'''

## knn regressor is not good
Out[71]:
"%matplotlib inline\nplt.figure(figsize=(10, 6))\nplt.xlabel('dist_travlled')\nplt.ylabel('price')\nplt.scatter(distance,actual,color='red')\nplt.scatter(distance,y_pred,color='blue')\n\nplt.legend(['Actual price ','Predicted Price'])\nplt.show()"

decision tree regressor¶

In [72]:
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, r2_score
In [ ]:
 
In [73]:
decision_tree_regressor = DecisionTreeRegressor(random_state=42)
In [74]:
decision_tree_regressor.fit(X_train, Y_train)
Out[74]:
DecisionTreeRegressor(random_state=42)
In [75]:
y_pred = decision_tree_regressor.predict(X_test)
In [76]:
mse = mean_squared_error(Y_test, y_pred)
r2 = r2_score(Y_test, y_pred)
print('MAE',mean_absolute_error(Y_test,y_pred))
MAE 1921.797128884682

finding accuracy¶

In [77]:
print(r2)
0.9337260347094475
In [78]:
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')

plt.legend(['Actual price ','Predicted Price'])
plt.show()

Conclusion¶

Random forest and decision tree are giving high accuracy than linear and knn regressor and desicion tree regressor is also showing better result, and also done randomized search CV for randomforest model by taking parameters randomly also gave good results¶
In [ ]:
 
In [ ]: